1. Technical Field
The invention generally relates to predictive model development. More particularly, the invention relates to a method and system for modeling impact of future actions that are unknown at scoring date in quality assessments at the scoring date.
2. Background Information
Model based decisions are often based on data in which the intention is that the data changes as a consequence of the use of data. A frozen score, therefore, predicts an outcome assuming that all else is held equal. The technology herein goes further to include a person's tolerance to the range of possible changes that may occur shortly after the scoring date. This approach is referred to as future action impact modeling, in that it includes the range of possible future behavior into a fixed observation date occurring before these potential behaviors occur.
Standard modeling problems typically involve two snapshots. One is called the pred (predictive) snapshot, referred to as the scoring date in implementation; the other, the perf. (performance) snapshot. Typically, at time one (T1), a model is built to predict performance at time two (T2). This is true of any standard analytic model where at time two, there would be one of a variety of performances—for example, risk or revenue. This modeling approach is common not just in financial services and credit, but in insurance and other areas. Performances are generally denoted by zeroes and ones—representing—in the credit scoring field, for example—“goods” and “bads.” A “bad” may be a default, or a delinquency, for example; a “good” is payment as agreed. Using credit bureau data and risk scores, it is possible to paint a picture of one's credit at one time, and then to predict what the picture will look like at another time.
These models, however, only include the consumer's history at the predictive data and prior, and as such, cannot address the relative impact of debts incurred shortly after the scoring date. This is an important question because most uses of a predictive score are designed to make a credit decision where a change after the scoring date is particularly likely.
Typically, a lender gets a score at a first time to make a lending decision at the first time. Subsequently, the lender desires to predict the borrower's performance. In attempting to predict borrower performance, the lender may rank order borrowers based on a score such as a FICO (FAIR ISAAC CORPORATION, Minneapolis Minn.) score, a behavior score, some other kind of risk or revenue score in order to predict borrower performance. However, such scores do not reflect events, such as incurring additional debt, after the scoring date.
Experience has shown that credit bureau data is considerably more reliable than consumer-supplied data for determining creditworthiness. Lenders, therefore often use risk scores calculated from credit bureau data in evaluating creditworthiness, for example the FICO (FAIR ISAAC CORPORATION, Minneapolis Minn.) score. The FICO score predicts default risk from a credit bureau report snapshot. While the FICO score accurately assesses default risk based on static credit bureau information, it does not consider information not yet represented on credit reports, such as new debt. Additionally, while people with the same score represent the same risk, different consumer profiles in which baseline risk is the same get the same score even though these consumers may respond differently to subsequent changes in credit.
In any scoring system, there are a variety of different profiles which numerically equate to the same relative odds. Because there are a variety of such profiles, there is opportunity to further identify records with a similar probability of performance, i.e. score, but differ in their tolerance to the range of future possible behaviors.